README.md
In hjunwoo/slimy: slimy: (Monte Carlo) SampLIng-based Method for causalitY
slimy
(SampLIng-based Method for causalitY)
Institute for Health Informatics, University of Minnesota
- slimy is a collection of Markov chain Monte Carlo (MCMC)
samplers, implemented in R, designed to infer directed acyclic graphs
representing causal relationships between nodes.
- Both discrete (multinomial) and continuous (Gaussian) data can be treated.
- Default algorithms include Gibbs sampling using parent set-based blocking
(Goudie and Mukherjee), which enables efficient sampling of
directed acyclic graph (DAG) space for a large number of variables.
hjunwoo/slimy documentation built on May 26, 2019, 3:32 a.m.
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slimy
(SampLIng-based Method for causalitY)
Institute for Health Informatics, University of Minnesota
- slimy is a collection of Markov chain Monte Carlo (MCMC) samplers, implemented in R, designed to infer directed acyclic graphs representing causal relationships between nodes.
- Both discrete (multinomial) and continuous (Gaussian) data can be treated.
- Default algorithms include Gibbs sampling using parent set-based blocking (Goudie and Mukherjee), which enables efficient sampling of directed acyclic graph (DAG) space for a large number of variables.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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